water
Article
Effects of Urban Non-Point Source Pollution from
Baoding City on Baiyangdian Lake, China
Chunhui Li 1,2 , Xiaokang Zheng 1,3, *, Fen Zhao 1, *, Xuan Wang 1,2 , Yanpeng Cai 2 and
Nan Zhang 1
1
2
3
*
Ministry of Education Key Lab of Water and Sand Science, School of Environment,
Beijing Normal University, Beijing 100875, China; [email protected] (C.L.);
[email protected] (X.W.); [email protected] (N.Z.)
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University,
Beijing 100875, China; [email protected]
Yellow River Engineering Consulting Co. Ltd., Zhengzhou 450003, China
Correspondence: [email protected] (X.Z.); [email protected] (F.Z.); Tel.: +86-10-5880-2928 (F.Z.)
Academic Editors: Karim Abbaspour, Raghavan Srinivasan, Saeid Ashraf Vaghefi, Monireh Faramarzi and
Lei Chen
Received: 29 December 2016; Accepted: 30 March 2017; Published: 1 April 2017
Abstract: Due to the high density of buildings and low quality of the drainage pipe network in
the city, urban non-point source pollution has become a serious problem encountered worldwide.
This study investigated and analyzed the characteristics of non-point source pollution in Baoding
City. A simulation model for non-point source pollution was developed based on the Stormwater
Management Model (SWMM), and, the process of non-point source pollution was simulated for
Baoding City. The data was calibrated using data from two observed rainfall events (25.6 and 25.4 mm,
the total rainfall on 31 July 2008 (07312008) was 25.6 mm, the total rainfall amount on 21 August
2008 (08212008) was 25.4 mm) and validated using data from an observed rainfall event (92.6 mm,
the total rainfall on 08102008 was 92.6 mm) (Our monitoring data is limited by the lack of long-term
monitoring, but it can meet the requests of model calibration and validation basically). In order
to analyze the effects of non-point source pollution on Baiyangdian Lake, the characteristics and
development trends of water pollution were determined using a one-dimensional water quality
model for Baoding City. The results showed that the pollutant loads for Pb, Zn, TN (Total Nitrogen),
and TP (Total Phosphorus) accounted for about 30% of the total amount of pollutant load. Finally,
applicable control measures for non-point source pollution especially for Baoding were suggested,
including urban rainwater and flood resources utilization and Best Management Practices (BMPs) for
urban non-point source pollution control.
Keywords: Baoding City; SWMM; non-point source (NPS) pollution; Baiyangdian Lake; rainfall-runoff
1. Introduction
Over the past decades, high-speed urbanization has led to increasing imperviousness in
urban-underlying surface in many parts of the world [1]. An increase in imperviousness results
in marked changes in water circulation patterns and may result in higher risks of flood disaster in
urban areas [2–4]. Particularly, this problem is exacerbated by increases in urban dust levels due
to a sharp growth of the urban population and industrial activities in developing countries such as
China [5,6]. Since a large quantity of urban dust is transported into water bodies by rainfall-runoff
processes, this might cause serious deterioration of urban water quality. This process has been
recognized as urban non-point source pollution and has become a great threat to the urban water
environment [7–9]. This pollution process is very complex because it involves diverse pollutants
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that originate from various non-point sources in an urban environment, such as suspended solids,
organic materials, nutrients, heavy metals, and pesticide residue [10–13]. Health-related conditions
for both human beings and aquatic organisms can be greatly affected by this type of pollution due to
stormwater and the associated urban non-point sources [14]. This is particularly true in many cities in
China, which is one of the most expedite economic and industrialized countries in the world. Therefore,
urban stormwater can lead to both qualitative and quantitative problems in the receiving waters.
In recent years, the management of the quantity and quality of stormwater runoff from urban
areas has become a complex task and an increasingly important environmental issue for urban
communities [15–17]. To deal with this issue, computer-aided models are extremely useful for
simulating and predicting the quantity and quality of urban stormwater. For example, since the
1970s, the United States and other developed countries have started to apply mathematical models
to simulate the processes of urban rainfall-runoff. In addition, these models were widely used for
evaluating effects of surface runoff pollution on various drainage systems and the corresponding
receiving water bodies. These widely used urban stormwater models include the Source Loading and
Management Model (SLAMM), the Storage, Treatment, Overflow, Runoff Model (STORM), the Model
for Urban Sewers (MOUSE), the Stormwater Management Model (SWMM), and various derivative
models [18–20]. Among them, SWMM is a computerized program that can assess the impacts of
surface runoff pollution and evaluate the effectiveness of many mitigation strategies. It was first
developed in 1971 and has undergone several major upgrades since then [18,21]. The current edition,
Version 5, which runs under Windows, is a complete revision of the previous release. It is widely used
throughout the world for supporting planning, analysis, and design related to stormwater runoff, and
it integrates sewers, sanitary sewers, and other drainage systems in urban areas [22].
Research into the applications of the SWMM model in China has been conducted for decades.
The SWMM model has been described as the classic non-point source pollution model [23,24].
The Morris screening method was used for a local sensitivity analysis of the parameters in the
hydrologic and hydraulic modules of the SWMM model, in order to identify the sensitivity of the
model parameters and perform an uncertainty analysis [25] (Section 3, the Electronic Supplementary
Information, ESI). The results showed that the impact factors of the three most sensitive parameters
were coefficients of imperviousness [26].
Baiyangdian Lake, situated centrally in the North China Plain, is the kidney of North China.
The lake plays vital roles in flood reservation and environmental pollution decomposition [27].
However, Baoding City is the largest city in the upper basin, the human activities in the city have an
important impact on the ecological environment of the Baiyangdian Lake. The quantity of sewage
that drained into Baiyangdian Lake from Baoding City was about 25 to 33.6 × 104 t per day, and
the quantity of sewage that drained into lake by secondary storm water runoff can reach about 25 to
30 × 104 t per day [28]. A large number of contaminants draining into Baiyangdian Lake is a serious
threat to the Baiyangdian water ecological environment. Therefore, it is of great practical significance
to research the impact of non-point source pollution of Baoding City on the water environment of
Baiyangdian Lake.
Therefore, the objective of this research is to understand the process of non-point source pollution
in Baoding City and the concentration of non-point source pollutants at the outfall of the catchment.
Our monitoring data is limited by the lack of long-term monitoring, the use of only three sets of
data, two used to calibrate and the other one to validate the model, is rather insufficient. However,
it was already demonstrated by Di Modugno et al. that even a minimum amount of experimental
observations may provide relevant information necessary to enhance design procedures and to improve
the efficiency of systems aimed at first flush separation, storage, and treatment [29]. Our main objectives
are to (1) develop a simulation model for non-point source pollution based on SWMM, and based
on the model results, analyze the characteristic effects of the non-point source pollution in an urban
catchment in Baoding City and (2) reveal the effects of the non-point source pollution of Baoding City
2. Materials and Methods 2.1. Overview of Baoding City and Baiyangdian Lake Baoding City is located in the mid‐east of Hebei Province, and Baiyangdian Lake is located in the east, Taihang Mountain in the west, and the vast and fertile Hebei Great Plains in the north and 2, with a population of 1.07 million people. The city is Water 2017,
9, 249
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south (Figure 1). The urban area is 312.3 km
located in the plains, and the terrain slopes from northwest to southeast and extends to the Taihang Mountain area in the northwest with a gentle slope. The city has 12 rivers and streams, of which the on Baiyangdian
Lake with a one-dimensional water quality model, the results will be useful for better
Caohe and Tanghe River cross the city and flow into Baiyangdian Lake, which is called the “Pearl of control
of
urban
non-point source pollution and water environmental recovery of Baiyangdian Lake.
North China” [28]. With the rapid economic development, water resource shortages, water consumption, and 2. Materials
and Methods
sewage emissions in Baoding City continue to increase, and because the pollution control measures are less developed, several water‐related environmental problems have become more significant. 2.1. Overview
of Baoding City and Baiyangdian Lake
The first problem is the pollution of the flood drainage system. Sewage is discharged into the rivers, causing the destruction of the water environment. Baoding
City is located in the mid-east of Hebei Province, and Baiyangdian Lake is located in
Lake Baiyangdian, situated centrally in the North China Plain, is located 130 km south of Beijing the east, Taihang
Mountain in the west, and the vast and fertile Hebei Great Plains in the north and
2, with a catchment area of 31,200 m2. The lake depth (Figure 1). The surface area of the lake is 366 km
south (Figure 1). The urban area is 312.3 km2 , with a population of 1.07 million people. The city is
varies according to the hydrologic conditions, but is usually less than 2.0 m [27,30,31]. The annual located in the plains, and the terrain slopes from northwest to southeast and extends to the Taihang
mean precipitation is less than 450 mm, and the annual mean ambient temperature is less than 17 °C in Mountain
area
in the With northwest
with
a gentle
slope.
The
city
has
12 lake rivers
andvital streams,
9 m
3, the climate changes. average annual runoff of 3.57 × 10
plays roles of
in which
flood the
Caohe
and Tanghe River cross the city and flow into Baiyangdian Lake, which is called the “Pearl of
reservation, environmental pollution decomposition, etc. Moreover, the lake is a monomictic lake with Northonly one entrance accepting pollutant emissions from Fuhe River [24] (Figure 1). China” [28].
Figure 1. Monitoring sections location in Baiyangdian Lake. Figure
1. Monitoring sections location in Baiyangdian Lake.
2.2. Available Data With the rapid economic development, water resource shortages, water consumption, and sewage
The data of rainfall on 31 July 2008 (07312008), 21 August 2008 (08212008), and 10 August 2008 emissions in Baoding City continue to increase, and because the pollution control measures are less
(08102008), was measured by rain gauge. Precipitation was recorded every 5 min. The total rainfall developed, several water-related environmental problems have become more significant. The first
on 07312008 was 25.6 mm, but only lasted for approximately 2 h and had a high rainfall intensity. problem
is the pollution of the flood drainage system. Sewage is discharged into the rivers, causing
The total rainfall amount on 08212008 was 25.4 mm, lasted for approximately 7 h, but had a much the destruction
of the
water environment.
lower rainfall intensity. The total rainfall on 10 August 2008 (08102008) was 92.6 mm, lasted Lake Baiyangdian, situated centrally in the North China Plain, is located 130 km south of Beijing
(Figure 1). The surface area of the lake is 366 km2 , with a catchment area of 31,200 m2 . The lake depth
varies according to the hydrologic conditions, but is usually less than 2.0 m [27,30,31]. The annual
mean precipitation is less than 450 mm, and the annual mean ambient temperature is less than 17 ◦ C
in climate changes. With average annual runoff of 3.57 × 109 m3 , the lake plays vital roles in flood
reservation, environmental pollution decomposition, etc. Moreover, the lake is a monomictic lake with
only one entrance accepting pollutant emissions from Fuhe River [24] (Figure 1).
2.2. Available Data
The data of rainfall on 31 July 2008 (07312008), 21 August 2008 (08212008), and 10 August 2008
(08102008), was measured by rain gauge. Precipitation was recorded every 5 min. The total rainfall on
07312008 was 25.6 mm, but only lasted for approximately 2 h and had a high rainfall intensity. The total
rainfall amount on 08212008 was 25.4 mm, lasted for approximately 7 h, but had a much lower rainfall
intensity. The total rainfall on 10 August 2008 (08102008) was 92.6 mm, lasted approximately seven
hours and was the strongest rainstorm that occurred from July to September 2008.
Our monitoring data is limited by the lack of long‐term monitoring, the use of only three sets of data, two used to calibrate and the other one to validate the model, is rather insufficient. However, it can meet the requests of model calibration and validation at a basic level [29]. 2.3. Methods Water 2017, 9, 249
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To analyze the process of non‐point source pollution of Baoding City and the effects of non‐point source pollution on Baiyangdian Lake, we use the Stormwater Management Model Our monitoring
data is limited by
the lack
of long-term
use study, of onlythe three
sets of
(SWMM) and the one‐dimensional water quality model. In monitoring,
the course the
of the technical data,
two
used
to
calibrate
and
the
other
one
to
validate
the
model,
is
rather
insufficient.
However,
route is as follows (Figure 2). it canAccording to the hydrological parameters, quality parameters and hydraulic parameters of the meet the requests of model calibration and validation at a basic level [29].
model, the sensitivity analysis of the model parameters was carried out, and the SWMM model of 2.3. Methods
Baoding City was established after repeated calibration and verification. The simulation process of To analyze the process of non-point source pollution of Baoding City and the effects of non-point
non‐point source pollution in Baoding City is divided into typical simulation of stormwater runoff source
pollution on
Lake, we useThe the Stormwater
and
and simulation of Baiyangdian
annual rainfall‐runoff. influence of Management
stormwater Model
runoff (SWMM)
and annual the
one-dimensional
water
quality
model. In
thewater courseenvironment of the study,
the studied technical
is as
rainfall‐runoff pollution on the Baiyangdian Lake were by route
analyzing follows
(Figure 2).
the characteristics of pollution reduction along the Fuhe River. Figure 2. Diagram of the technical route. Figure
2. Diagram of the technical route.
2.3.1. SWMM Model According to the hydrological parameters, quality parameters and hydraulic parameters of the
SWMM, a dynamic rainfall‐runoff simulation model, used out,
for single event or long‐term model,
the sensitivity
analysis
of the model
parameters
was is carried
and the
SWMM
model of
(continuous) simulation of runoff quantity calibration
and quality from primary The
urban areas. The runoff Baoding
City was
established
after repeated
and
verification.
simulation
process
of
component of SWMM operates on a collection of sub‐catchment areas that receive precipitation and non-point
source pollution in Baoding City is divided into typical simulation of stormwater runoff and
generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through a simulation
of annual rainfall-runoff. The influence of stormwater runoff and annual rainfall-runoff
system of channels, storage/treatment devices, pumps, and SWMM tracks the pollution
onpipes, the Baiyangdian
Lake water environment
were
studied
by regulators. analyzing the
characteristics
of
quantity and quality of runoff generated from each sub‐catchment, and the flow rate, flow depth, pollution reduction along the Fuhe River.
and quality of water in each pipe and channel during a simulation period are comprised of multiple 2.3.1.
SWMM
Model
time steps. This section briefly describes the methods SWMM uses to model stormwater runoff quantity and quality through the following two processes [21]. SWMM, a dynamic rainfall-runoff simulation model, is used for single event or long-term
(continuous) simulation of runoff quantity and quality from primary urban areas. The runoff
component of SWMM operates on a collection of sub-catchment areas that receive precipitation
and generate runoff and pollutant loads. The routing portion of SWMM transports this runoff through
a system of pipes, channels, storage/treatment devices, pumps, and regulators. SWMM tracks the
quantity and quality of runoff generated from each sub-catchment, and the flow rate, flow depth, and
quality of water in each pipe and channel during a simulation period are comprised of multiple time
Water 2017, 9, 249
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steps. This section briefly describes the methods SWMM uses to model stormwater runoff quantity
and quality through the following two processes [21].
(1)
Runoff Simulation
SWMM uses the St. Venant equations for flow simulation [32,33]. The St. Venant equations
represent the principles of conservation of momentum (Equation (1)) and conservation of
mass (Equation (2))
∂y v ∂v
1 ∂v
+
+
= So − S f
(1)
∂x
g ∂x
g ∂t
∂Q ∂A
+
=0
∂x
∂t
(2)
where y is the water depth, m; v is the velocity, L·t−1 ; x is the longitudinal distance, m; t is the time,
s; g is the gravitational acceleration, 9.8 m/s; So is channel slope, dimensionless; Sf is friction slope,
dimensionless; A is the area of the flow cross-section and a function of y based on the geometry of the
conduit, m2 ; Q is the discharge and it is equal to A × v, m3 ·s−1 .
Equation (1) represents hydrostatic pressure, convective acceleration, local acceleration, and
gravity and frictional forces, respectively. Representing the effects of turbulence and viscosity, the
friction slope (Sf ) is calculated in SWMM using Manning’s equation [18]:
Sf =
Q2
1 2 4/3
A R
n2
(3)
where n is Manning’s roughness coefficient, t·L−1/3 ; R is hydraulic radius, m; Q and A are
defined previously.
Equation (3) is substituted into Equation (1), and the resulting equation is solved for Q:
Q=
1/2
1
∂y
v ∂v
1 ∂v
AR2/3 (
+
+
− SO )
n
∂x
g ∂x
g ∂t
(4)
Since there is no known analytic solution, an iterative finite-difference method is applied to
Equations (2) and (4) to solve the equations. For each time step, the discharge, area, and water depth
(y) at the outlet of each conduit are derived.
(2)
Water quality simulation
In this paper, the exponential function is used as the surface pollutant build-up and wash-off
algorithms of the SWMM model, and the build-up and wash-off algorithms used to simulate these
two processes (Section 2 in the ESI).
Water quality routing within conduit links assumes that the conduit behaves as a
continuously-stirred tank reactor (CSTR). Although the assumption of a plug flow reactor might
be more realistic, the differences will be small if the travel time through the conduit is on the same
order as the routing time step. The concentration of a constituent exiting the conduit at the end of a
time step is determined by integrating the conservation of mass equation and using average values for
quantities that might change over the time step, such as flow rate and conduit volume [22].
Solute transport is simulated with the assumption of complete and instantaneous mixing within
each element of the sewer system. The instantaneous mixing assumption introduces artificial
dispersion; however, as the number of conduit elements is increased within a system, solute transport
is represented by pure advection [22]. The overall transport of solutes through the system is executed
through a mass balance calculation that incorporates decay. The concentrations of solutes are
determined using the finite difference form of the continuity equation [18]:
∂(Vc)
= Qi ci − Qo co − kcV + s
∂t
(5)
Water 2017, 9, 249
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where c is the concentration in the mixed volume, m·L−3 ; V is the volume, L3 ; t is the time, t; Qi and
Qo are the inflow (i) and outflow (o) rate, L3 ·t−1 ; ci and co are the concentrations of the influent and
effluent, m·L−3 ; k is the decay constant, t−1 ; s is the source (or sink), m·t−1 .
Assuming complete mixing and applying a finite-difference scheme, Equation (5) becomes:
c j +1 =
2
(c j (Vj ( ∆t
− ( D1 + D2 )) − Qo,j ) + (ci,j + Qi,j ) + (ci,j+1 + Qi,j+1 ) + D2 · S(Vj + Vj+1 ))
2
(Vj+1 · ( ∆t
+ ( D1 + D2 )) + Qo,j+1 )
(6)
where j is the time-step number; D1 is the decay constants, t−1 ; D2 is the growth constant, t−1 ;
S = maximum growth, m·L−1 ; Dt is the time increment, t, and the other parameters were defined
with Equation (5).
2.3.2. One-Dimensional Water Quality Model
Sewage and rainwater from Baoding City flow into the Fuhe River and a biochemical reaction
occurs gradually in the flow process under the action of natural microorganisms. As a result, the main
pollutant COD is decomposed as the flow process has been lengthened. For the Fuhe River with small
ratio of width to depth, pollutants can be mixed in these sections (Jiaozhuang, Wangting, Anzhou,
Nanliuzhuang) in a relatively short period of time. A one-dimensional water quality model that can
simulate the migration of pollutants along the river longitudinal has been described in [34,35] and is
defined as:
x
(7)
C ( x ) = C0 exp(−k · )
u
where C ( x ) is the contaminant concentration of the control section, mg/L; C0 is the contaminant
concentration of the initial section, mg/L; k is the self-purification capacity of the pollution, 1/d; x is
the longitudinal distance of the control section of the downstream section of the sewage outfalls, m; u
is the average flow velocity of the polluted belts along the river banks, m/s.
3. Results and Discussions
3.1. Model Calibration and Model Validation
In this study, the drainage system of Baoding City was generalized based on an analysis of the
drainage system and field reconnaissance of the study area. The entire city was divided into three
sub-watersheds: a middle sub-watershed, a southern sub-watershed, and a northern sub-watershed,
based on the actual drainage system (Figure 3). The rainfall-runoff of the middle sub-watershed
flowed into Yimuquan River, Hou River, and Qingshui River, and then flowed together into the Fuhe
River. The urban rainfall-runoff of the southern sub-watershed and northern sub-watershed was also
generalized to flow into flood embankments as gravity flow and then flow into the Fuhe River, without
regard for processed rainfall-runoff through the sewage treatment plant.
A total of 447 rainwater pipe nodes and 447 rainwater pipes were generalized (including rainwater
pipes, sewage pipes, and open channels). The diameter of rainwater pipes ranged between 400 and
1400 mm, and some pipes had a rectangular cross-section of 2000 mm × 2000 mm, while the bottom
width of open channels varied between 5 and 14 m. Furthermore, three outlets were generalized,
Node_556, Node_557, and Node_558. Node_557 was the Jiaozhuang section on the Fuhe River of
the middle sub-watershed, Node_556 and Node_558 were the sections on the northern and southern
flood embankments, respectively, and they represented the outlets of the northern and southern
sub-watersheds (Figure 4, Tables S1 and S2 in the ESI). Based on the generalization of the drainage
system, the boundaries of trunk roads and streets in conjunction with field reconnaissance and research,
and taking into account a topographic map and the convergence characteristics of Baoding City, the
whole urban catchment was divided into 450 sub-catchments, with a total area of 130.76 km2 (Figure 5
and Tables S3 in the ESI).
Water 2017, 9, 4 7 of 18 Water
2017, 9, 249 7 of 18
Water 2017, 9, 4 7 of 18 Baoding City, the whole urban catchment was divided into 450 sub‐catchments, with a total area of 130.76 km2 (Figure 5 and Tables S3 in the ESI). Baoding City, the whole urban catchment was divided into 450 sub‐catchments, with a total area of 130.76 km2 (Figure 5 and Tables S3 in the ESI). Figure 3. Drainage system diagram of Baoding City. Figure 3. Drainage system diagram of Baoding City.
Figure 3. Drainage system diagram of Baoding City. Figure 4. Drainage system generalization of Baoding City. Figure 4. Drainage system generalization of Baoding City. Figure 4. Drainage system generalization of Baoding City.
Water 2017, 9, 249
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Water 2017, 9, 4 8 of 18 S_203
S_215
S_214
S_212
S_229
S_863
S_257 S_256
S_240
S_235
S_245
S_246
S_281
S_244
S_241
S_263
S_280 S_274
S_275 S_276
S_250 S_234 S_224
S_242
S_300
S_316 S_278S_270
S_248
S_205
S_259 S_264 S_255 S_285S_324
S_323
S_299 S_312
S_318
S_230
S_260 S_265 S_238
S_291
S_253S_317
S_311
S_310
S_298S_304 S_239 S_254 S_315
S_225 S_269
S_268
S_204
S_867
S_430
S_424
S_436 S_444
S_217
S_206
S_313S_309
S_273
S_314
S_321
S_267
S_220S_308S_293S_305 S_279 S_272
S_322
S_289
S_303
S_296 S_290
S_295
S_306 S_252
S_320
S_307
S_302S_301
S_319
S_231
S_435S_427S_216
S_286
S_251
S_666
S_282
S_232
S_223
S_201
S_227
S_439
S_202S_209 S_213
S_218
S_687
S_694
S_669S_228 S_266
S_219
S_769S_764
S_670
S_761
S_845S_843
S_243
S_858
S_859S_447S_450
S_287
S_297
S_846S_844
S_294
S_773
S_689S_685 S_677
S_449
S_423
S_766
S_226
S_775
S_673
S_432
S_448S_763
S_836
S_249
S_684
S_777
S_674S_688
S_839
S_667
S_446
S_697
S_776S_767
S_284S_262 S_237
S_837
S_774
S_842 S_679S_691
S_283
S_236
S_692
S_696
S_695
S_838
S_429
S_840
S_693
S_762
S_841
S_442
S_768
S_680 S_690
S_675
S_292
S_277
S_434
S_772S_770
S_681
S_445
S_819 S_686
S_210 S_233 S_258
S_822
S_678
S_288
S_825 S_683
S_672
S_860
S_861
S_271
S_827
S_824 S_682
S_526
S_544
S_525
S_211
S_760 S_771S_765
S_823
S_514
S_518
S_222
S_826
S_428
S_554
S_584
S_517
S_709S_700
S_461
S_556
S_704
S_567
S_563
S_710
S_247
S_820 S_676
S_545
S_730
S_431
S_516
S_671 S_587
S_580
S_221 S_261
S_426
S_560
S_733
S_463
S_759
S_564
S_579
S_520
S_705
S_742
S_559
S_572
S_708
S_465
S_821
S_546
S_573 S_746
S_745
S_568
S_561
S_581
S_734
S_552
S_533
S_585
S_455 S_469
S_460S_459
S_668
S_600
S_598
S_562
S_818
S_594
S_530
S_593
S_706
S_590
S_550
S_731S_716
S_523
S_604
S_602
S_553
S_735
S_603
S_592
S_739
S_582
S_736
S_472 S_476 S_458
S_524
S_557
S_468
S_454
S_591
S_589
S_574
S_601
S_565
S_583
S_470
S_539
S_570
S_558
S_707 S_741
S_528 S_576
S_207
S_491
S_479
S_575
S_595
S_571
S_483
S_537
S_743 S_728
S_484
S_453
S_569
S_726 S_715
S_471
S_481
S_492
S_489
S_538
S_577
S_597
S_531
S_540
S_578
S_599
S_596
S_723
S_740
S_475 S_473 S_485
S_747
S_480
S_200
S_477
S_541
S_588
S_566
S_535
S_748
S_486
S_586
S_712
S_744
S_753
S_487
S_751
S_555
S_738
S_737
S_490
S_452
S_467
S_478
S_749
S_750
S_752
S_721
S_548S_551
S_720
S_482
S_542
S_529
S_729
S_549
S_474
S_456
S_464
S_488
S_732
S_522
S_462
S_547
S_466
S_725
S_722
S_543 S_527S_719S_727
S_380S_378 S_457
S_379
S_699
S_718
S_532
S_536
S_711
S_394
S_521S_702 S_724
S_377
S_391
S_398
S_534
S_326
S_717
S_392
S_397
S_387
S_515S_519 S_703 S_713
S_714
S_384
S_862
S_701
S_381
S_393
S_367
S_366
S_372
S_376
S_388
S_395
S_389
S_368
S_396 S_386
S_383
S_856
S_855
S_852S_854
S_433
S_866
S_865
S_365
S_373
S_382
S_847
S_848
S_375
S_369
Figure 5. Sub‐catchment diagram of Baoding City. Figure 5. Sub-catchment diagram of Baoding City.
3.1.1. Model Calibration 3.1.1. Model Calibration
(1) Parameters of SWMM (1)
Parameters
of SWMM
SWMM parameters include hydrological, hydraulic, and water quality parameters. It is relatively easy to determine the hydraulic parameters by surveying pipes and networks. However, SWMM parameters include hydrological, hydraulic, and water quality parameters. It is relatively
the hydrological parameters are relatively difficult to determine. The parameters in this study were easy obtained through the model handbook and field surveys. Table 1 lists the major hydrological and to determine the hydraulic parameters by surveying pipes and networks. However, the
hydrological
parameters are relatively difficult to determine. The parameters in this study were
hydraulic parameters, their ranges, and methods for determining the parameters. obtained through the model handbook and field surveys. Table 1 lists the major hydrological and
hydraulicTable 1. Major hydrological, hydraulic parameters of Stormwater Management Model (SWMM) and parameters, their ranges, and methods for determining the parameters.
their range and obtain methods. Major hydrological, hydraulic
Management Model
(SWMM) and
Table
NO. 1.Parameter Meaningparameters of Stormwater
Data Range
Data Source their
range
and
obtain
methods.
1 Manning‐N Manning coefficient of Pipe 0.005~0.04 SWMM handbook 2 N‐Imperv Mannings N of impervious area 1
3 2
3
4 4 5
Manning-N
N‐perv N-Imperv
N-perv
S‐Imperv S-Imperv
S-perv
Manning coefficient of Pipe
Mannings N of pervious area 6 5 S‐perv Pct-Zero
NO.
7
8 6 9
7 10
8 9 1210 11
11 Parameter
MaxRate
Pct‐Zero MinRate
Decay
MaxRate Imperv (%)
Meaning
Mannings N of impervious area
Mannings N of pervious area
Depression storage on impervious Depression storage on impervious area
area Depression storage on pervious area
Depression storage on pervious Percent of impervious area with no
area storage
depression
Maximum rate on infiltration curve
Percent of impervious area with no Minimum
rate on infiltration curve
depression storage Decay constant for infiltration curve
Maximum rate on infiltration curve
Percent of impervious area
0.005~0.04 Data Range
0.005~0.04
0.1~0.8 0.005~0.04
0.1~0.8
0.2~2 (mm) 0.2~2 (mm)
2~10 (mm)
2~10 (mm) 50~80 (%)
3~50 (mm/h)
50~80 (%) 1~3 (mm/h)
2~7
field survey, SWMM Data Source
handbook field survey, SWMM SWMM handbook
field survey,
SWMM handbook
handbook field
survey, SWMM handbook
field survey, SWMM field survey, SWMM handbook
handbook field survey, SWMM handbook
field survey, SWMM field survey,
SWMM handbook
handbook SWMM handbook
field survey, SWMM SWMM
handbook
handbook SWMM handbook
SWMM handbook field survey, Google Earth
SWMM handbook GIS
SWMM handbook GIS
field survey, Google Earth 3~50 (mm/h) 10~90 (%)
MinRate Minimum rate on infiltration curve 1~3 (mm/h) depends
on the area
Width
Width of overland flow path
Decay Decay constant for infiltration curve
2~7 of sub-catchment
Slope
Average surface slope (%)
0.1~2 (%)
Imperv (%) Percent of impervious area 10~90 (%) depends on the area Width Width of overland flow path of sub‐catchment parameters
for Average surface slope (%) the accumulation of pollutants
in water quality
Slope 0.1~2 (%) GIS The
portion
of the model
12 GIS were obtained by measurements, but empirical values were used for the scouring parameters.
The sub-catchments in the SWMM model were divided into four types of land-use including business
areas, residential areas, industrial areas, and green areas. Moreover, the main pollution factors
including COD, TN, TP, Pb, and Zn, which were produced by rainwater mixing with the urban dust,
were simulated in the model. The parameters for pollutant accumulation and erosion in different
Water 2017, 9, 249
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land-use types are shown in Tables 2 and 3, wash off exponent means the runoff exponent in wash
off function, and wash off coefficient means wash off coefficient or Event Mean Concentration (EMC).
The cumulative amount of surface pollution is directly related to land use condition, greening condition,
traffic condition, rainfall interval and rainfall intensity. The distribution of dust accumulation on the
surface is: industrial area > traffic area > residential area > green area. In this study, we regard the
different land use types had the same cumulative rate constants and half-saturated accumulation times.
The cumulative rate constant was 0.5 and the half-saturated accumulation time was 10 days.
Table 2. Maximum accumulation quantity of pollutants on different land use types kg/hm2 .
Area
COD
TN
TP
Pb
Zn
Commercial area
Residential area
Industrial area
Green area
46
58
43
20
15.6
8.6
10.3
14.5
0.64
0.24
0.38
0.17
0.12
0.056
0.12
0.045
0.62
0.22
0.22
0.11
Table 3. Wash off parameters of pollutants on different land use types.
(2)
Area
Parameter
COD
TN
TP
Pb
Zn
Commercial
area
wash off coefficient
wash off exponent
0.003
1.4
0.004
1.8
0.004
1.7
0.004
1.7
0.004
1.8
Residential
area
wash off coefficient
wash off exponent
0.003
1.4
0.004
1.8
0.002
1.7
0.004
1.7
0.004
1.8
Industrial
area
wash off coefficient
wash off exponent
0.003
1.4
0.004
1.8
0.004
1.7
0.004
1.7
0.004
1.8
Green area
wash off coefficient
wash off exponent
0.003
1.2
0.002
1.4
0.001
1.2
0.001
1.2
0.001
1.2
Sensitivity Analysis
A sensitivity analysis is used to study the impacts of parameters on the model output to identify
the key parameters of the model. The results from a sensitivity analysis by Huang [26] and Wang [27]
and the sensitivity ranking of the hydraulic and hydrological parameters in SWMM are listed in
Table 4. For different output variables, the sensitivity order of each parameter is also different, for
runoff coefficient, the order of parameter according to the sensitivity is Imperv(%) > S-Imperv >
Pct-Zero > N-Imperv > Width.
Table 4. Sensitivity ranking of hydraulic and hydrological parameters in SWMM.
(3)
Runoff Factor
1
2
3
4
5
Runoff Coefficient
Peak Discharge
Peak Discharge Time
Imperv(%)
Imperv(%)
Manning-N
S-Imperv
S-Imperv
N-Imperv
Pct-Zero
N-Imperv
S-Imperv
N-Imperv
Width
Width
Width
Pct-Zero
Pct-Zero
Calibration Results
The hydrology, hydraulic, and water quality parameters in SWMM were calibrated by using
rainfall data from 31 July 2008 (07312008) and 21 August 2008 (08212008). The rainfall amount was
nearly identical but had different characteristics. The total rainfall on 07312008 was 25.6 mm, but only
lasted for approximately 2 h and had a high rainfall intensity. The total rainfall amount on 08212008
was 25.4 mm, lasted for approximately 7 h, but had a much lower rainfall intensity. Therefore, these two
rainfall events were well suited to represent rainfall and could be used to calibrate the SWMM model
only lasted for approximately 2 h and had a high rainfall intensity. The total rainfall amount on 08212008 was 25.4 mm, lasted for approximately 7 h, but had a much lower rainfall intensity. Therefore, these two rainfall events were well suited to represent rainfall and could be used to Water
2017,
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by calibrate the SWMM model for Baoding City. First, the SWMM parameters were established using measured parameters and the empirical coefficient, and then the parameters were adjusted manually until the simulated results agreed well with the measured results. Based on the parameter for Baoding City. First, the SWMM parameters were established by using measured parameters and
the empirical
coefficient,
and then calibration the parameters
were adjusted
manually
until the simulated
results
sensitivity analysis, the parameter mainly focused on parameters with relatively high agreed well with the measured results. Based on the parameter sensitivity analysis, the parameter
sensitivity, while parameters with low sensitivity were adjusted roughly or the empirical coefficient calibration mainly focused on parameters with relatively high sensitivity, while parameters with low
was used directly. We used trial and error for adjustments and there was a good fit for the quantity sensitivity were adjusted roughly or the empirical coefficient was used directly. We used trial and error
and quality curves between the simulated and the measured processes. The results are shown in for adjustments and there was a good fit for the quantity and quality curves between the simulated
of 7. 07312008 rainfall and 08102008 Figures and
6 and 7. The processes.
simulated the measured
Thewater results quality are shownprocess in Figuresline 6 and
The simulated
water quality
process
line of
07312008
rainfall and water 08102008
rainstorm
fit wellline, with but the measured
water quality
rainstorm fit well with the measured quality process the simulated water quality process
line,
but
the
simulated
water
quality
process
line
of
08212008
did
not
fit
well.
process line of 08212008 did not fit well. Figure 6. Measured and simulated hydrograph of rainfall-runoff and water quality of 07312008 on
Figure 6. Measured and simulated hydrograph of rainfall‐runoff and water quality of 07312008 on Jiaozhuang
section.
Jiaozhuang section. Water 2017, 9, 249
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Water 2017, 9, 4 11 of 18 Figure 7. Measured and simulated hydrograph of rainfall‐runoff and water quality of 08212008 on Figure 7. Measured and simulated hydrograph of rainfall-runoff and water quality of 08212008 on
Jiaozhuang section.
Jiaozhuang section. 3.1.2. Model Validation
3.1.2. Model Validation The rainfall-runoff data from 10 August 2008 (08102008) was used to validate the simulated
The rainfall‐runoff data from 10 was
August was used validate the simulated results
of the SWMM
output,
which
based2008 on the(08102008) calibrated data.
The
total to rainfall
on 08102008
results of the SWMM output, which was based on the calibrated data. The total rainfall on 08102008 was 92.6 mm, lasted approximately seven hours and was the strongest rainstorm that occurred from
July to September 2008. The curves of measured and simulated runoff and water quality are shown
was 92.6 mm, lasted approximately seven hours and was the strongest rainstorm that occurred from in Figure 8.
July to September 2008. The curves of measured and simulated runoff and water quality are shown The relative error (RE) between the mean value of the measured data and the corresponding
in Figure 8. simulation data is used to test the goodness-of-fit.
The relative error (RE) between the mean value of the measured data and the corresponding simulation data is used to test the goodness‐of‐fit. Water 2017, 9, 249
12 of 18
Water 2017, 9, 4 12 of 18 Figure 8. Measured and simulated hydrograph of rainfall-runoff and water quality of 08102008 on
Figure 8. Measured and simulated hydrograph of rainfall‐runoff and water quality of 08102008 on Jiaozhuang section.
Jiaozhuang section. 3.2. Error Analysis
3.2. Error Analysis 3.2.1. Runoff
3.2.1. Runoff We obtained a good fit for the simulated and measured hydrograph data for the Jiaozhuang
but the simulated peak runoff volume was higher than the measured peak runoff volume by
We section,
obtained a good fit for the simulated and measured hydrograph data for the Jiaozhuang about 30%, and the simulated peak runoff occurred about 30 min prior to the measured peak runoff.
section, but the simulated peak runoff volume was higher than the measured peak runoff volume by However, the rate of decrease in the simulated peak runoff was also slower than the measured peak
about 30%, and the simulated peak runoff occurred about 30 min prior to the measured peak runoff. runoff. Moreover, there were certain differences between simulated and measured runoff volume
(Table
5). In the middle sub-watershed of Baoding City, rainwater flowed into the Yindingzhuang
However, the rate of decrease in the simulated peak runoff was also slower than the measured peak sewage plant there and, therefore,
the volume
was 80,000–100,000
cubic
meters. Byand adding
this portionrunoff volume of the
runoff. Moreover, were certain differences between simulated measured runoff to the other runoff data, the relative error of the SWMM results was acceptable. The calibrated
(Table 5). In the middle sub‐watershed of Baoding City, rainwater flowed into the Yindingzhuang relative error (RE* ) is shown in Table 5.
sewage plant and, therefore, the volume was 80,000–100,000 cubic meters. By adding this portion of the runoff to the other runoff data, the relative error of the SWMM results was acceptable. The calibrated relative error (RE*) is shown in Table 5. Table 5. Simulated runoff volume and measured runoff volume on Jiaozhuang section. Water 2017, 9, 249
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Table 5. Simulated runoff volume and measured runoff volume on Jiaozhuang section.
Analysis Period
Rain Time
Rainfall
(mm)
Simulated Runoff
Volume (104 m3 )
Measured Runoff
Volume (104 m3 )
RE
(%)
RE* (%)
07312008
08212008
08102008
25.6
25.4
92.6
33.40
28.83
94.19
18.87
15.87
60.97
−77.0
−81.6
−54.5
−24.3 ~−15.7
−20.8 ~−11.4
−36.6 ~−32.7
Calibration Period
Validation Period
3.2.2. Rainfall Runoff Coefficient
Based on the integrated rainfall-runoff coefficient point, the rainfall-runoff coefficients on 07312008,
08212008, and 08102008 were 0.376, 0.337, and 0.653, respectively, which was consistent with the
empirical rainfall-runoff coefficient values (0.3–0.7) of Baoding City (Section 4 and Table S4 in the ESI).
3.2.3. Water Quality
With regard to the water quality simulation, the results indicated that there was a better fit for the
simulated and measured water quality hydrograph data for 07312008 and 08102008 than for 08212008.
In the three simulation processes, the heavy metals Pb and Zn exhibited peak values, and the simulated
peak values and times of occurrence were consistent with the measured values and times, indicating
that the simulations for Pb and Zn were successful. However, concentrations of TN, TP, and COD did
not exhibit peak values. The concentrations of the three elements decreased during the rainfall-runoff
process and then gradually returned to levels seen prior to the rainfall-runoff event. The measured
times of recovery for the TN, TP, and COD concentrations were earlier than the simulated times,
especially for the rainfall event on 08212008. This was possibly due to the low rainfall intensity and
short duration; consequently, the conditions were not ideal for simulating erosion processes, and poor
simulation results were obtained. This was consistent with the results of the rainfall-runoff process
that showed that the rate of decrease of the simulated runoff was longer than the measured time.
This indicated that the SWMM model simulated water quality appropriately, especially for a rainfall
with heavy intensity.
3.2.4. Continuity Errors
The continuity errors for the three rainfall-runoff simulations, including the calibration and
validation periods, are shown in Table 6. The continuity error was lower for surface runoff and flow
routing than for the simulation of water quality. Research has suggested that the continuity error
should be less than 10% in SWMM [22]. The continuity error was acceptable for the simulation process
in this research, except for slightly higher values for the simulation of water quality on 07312008.
Table 6. Continuity errors of simulation.
Analysis Period
Rain Time
Surface Runoff
Flow Routing
Runoff Quality
Calibration period
07312008
08212008
−0.09%
−0.01%
−0.15%
−0.05%
14.23%
8.09%
Validation period
08102008
−0.06%
−0.07%
6.45%
3.3. Simulation Results
3.3.1. Simulation Results of 08102008 Storm Water Runoff
The results of the simulations for stormwater runoff on 08102008 are shown in Table 7 and are
as follows: (1) the runoff and output of the total non-point source pollution load were higher for the
northern and middle sub-watersheds than for the southern sub-watershed. This was not only due to
the larger area of the northern and middle sub-watersheds compared to the southern sub-watershed,
but also because the imperviousness was lower for the southern sub-watershed than for the other
Water 2017, 9, 249
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two sub-watersheds; (2) the overall stormwater runoff on 08102008 was 4.52 × 106 m3 , the average
flow was 50.83 m3 /s, and the maximum flow for the system was 136.48 m3 /s; and 3) the output values
for the non-point source pollution loads for Pb, Zn, TN, TP, and COD in the stormwater runoff on
08102008 were 145.50, 556.72, 29,412.84, 874.98 and 74,218.42 kg, respectively.
Table 7. Runoff and output of total non-point source pollution load on export section of 08102008.
Output Non-Point Source Pollution Load (kg)
Node
Average
Runoff (m3 /s)
Max. Runoff
(m3 /s)
Total Runoff
(104 m3 )
Pb
Zn
TN
TP
COD
Node_556
Node_557
Node_558
System
22.51
15.75
12.57
50.83
61.55
43.00
32.97
136.48
196.70
142.60
112.53
451.83
60.56
66.36
18.58
145.50
238.82
254.04
63.87
556.72
11453.24
13425.89
4533.71
29412.84
224.75
595.08
55.15
874.98
27970.87
35596.38
10651.17
74218.42
3.3.2. Simulation Results of 2008 Rainfall-Runoff
The results of the simulations for rainfall-runoff in 2008 are shown in Table 8. The annual rainfall
in 2008 was 564.3 mm, and the rainfall from June to September was 451 mm, which accounted for 80%
of the total annual rainfall. The simulation started on 1 January 2008, and ended on 31 December 2008.
Winter snowmelt runoff was not taken into account in the simulation. The infiltration losses were
103.48 mm, evaporation losses were 227.81 mm, and surface runoff was 233.39 mm. Furthermore, the
rainfall-runoff coefficient was 0.414 and the continuity error was −0.068%.
Table 8. Runoff and output of total non-point source pollution load on export section of 2008.
Output Non-Point Source Pollution Load (kg)
Node
Average
Runoff (m3 /s)
Max. Runoff
(m3 /s)
Total Runoff
(104 m3 )
Pb
Zn
TN
TP
COD
Node_556
Node_557
Node_558
System
0.38
0.99
0.16
1.53
18.48
14.97
8.6
41.81
1200.77
3116.06
496.30
4813.13
140.4
682.2
29.4
852.0
526.3
1747.7
88.9
2362.8
26127.3
1089088
7947.5
1123163
523.7
82203
92.1
82819
91681.3
2997034
27371.1
3116086
The annual rainfall-runoff in 2008 was 48.13 × 106 m3 , the average flow was 1.53 m3 /s, and the
maximum flow for the system was 41.81 m3 /s. The total runoff of the middle sub-watershed was
31.16 × 106 m3 , which accounted for 60% of the total annual runoff. This was due to the location of
the middle sub-watershed in the center of the city in an area of high imperviousness. For the 2008
rainfall-runoff, the annual output of the non-point source pollution loads for total Pb, Zn, TN, TP, and
COD was 852.0 kg, 2362.8 kg, 1,123,163 kg, 82,819 kg, and 3,116,086 kg respectively. The pollution
load of the middle sub-watershed accounted for 80% of those values. Furthermore, the pollution
load for TN and TP of the middle sub-watershed accounted for more than 90% of the total due to an
average imperviousness of as much as 85% for the central city, which was conducive to rainfall-runoff
generation. Another reason was the growth of inflow during dry season mainly generated in the
middle sub-watershed.
3.4. Influence of Non-Point Source Pollution from Baoding City on Baiyangdian Lake
3.4.1. Analysis of Non-Point Source Pollution Load in Baoding City
The total discharge and non-point source pollution loads of the rainstorm and annual rainfall
processes in Baoding City are shown in Table 9. The total discharge of a single storm on 20080810
was 4.52 ×106 m3 , and the output of the non-point source pollution load of the total Pb, Zn, TN, TP,
and COD was 145.5 kg, 556.7 kg, 29,412.8 kg, 875 kg, and 74,218.4 kg, respectively. Without any Best
Management Practices (BMPs), the total amounts of the output pollutants Pb, Zn, TN, TP, and COD
were almost doubled. With street-sweeping and other BMPs, the total output of the pollution loads
for Pb and Zn increased by more than 50%, while TN, TP, and COD approximately doubled in value.
Water 2017, 9, 249
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The removal efficiency of BMPs to Pb and Zn was approximately 10%, while for TN, TP, and COD,
the removal efficiency was only about 1%, demonstrating that BMPs had a positive influence on the
control of the non-point source pollution load in the basin.
Table 9. Total discharge and non-point source pollution load of Baoding City Basin.
Rain
Time
Rainfall
(mm)
Total Discharge
(104 m3 )
BMPs
08102008
92.32
451.83
1998
514.8
2008
564.3
Output Non-Point Source Pollution Load (kg)
Pb
Zn
TN
TP
COD
N
145.5
556.7
29,412.8
875.0
74,218.4
3453.31
N
Y
554.3
518.0
1560.9
1416.9
591,187.1
584,777.5
41,738.1
41,563.7
1,616,665.4
1,595,238.3
4813.13
N
Y
852.0
797.2
2362.8
2148.6
1,123,163.2
1,114,198.4
82,818.9
82,559.8
3,116,086.2
3,081,398.4
3.4.2. Reduction of Pollutants along the Fuhe River
The sewage and stormwater discharge from Baoding City through the city’s drainage network
and eventually pass through the Fuhe River into Baiyangdian Lake (Figures 1 and 2), which is located
approximately 45 km away. This small flow of sewage from Baoding City to Baiyangdian Lake requires
about two days, while a storm flood with large velocity requires about 5 h [28]. During this time, major
pollutants are degraded and reduced.
Based on a study of COD reduction along the Fuhe River [36,37] and Equation (7), the value for
k is 0.5 day−1 and u = 22.5 km/day Section 5 in the ESI). The coefficients were verified by using the
measured data shown in Table 10, and Equation (6) was well-suited to simulate the process of the
longitudinal attenuation of COD.
Table 10. Pollutant concentration of the sections mg/L.
Section
TN
TP
COD
Jiaozhuang
Wangting
Anzhou
Nanliuzhuang
49.33
27.45
25.55
15.31
2.34
1.22
1.21
0.34
97.75
42.74
31.84
32.52
3.4.3. Effects on the Water Environment of Baiyangdian Lake
There were seven sample locations for water quality monitoring in Baiyangdian Lake (Figure 1),
and the results are shown in Table 11.
Table 11. Water quality monitoring data of Baiyangdian Lake.
Parameters
Pb
TN
TP
Concentration (mg/L)
Zaolinhzuang Wangjiazhai
0.01
1.79
0.07
0.01
2.15
0.14
Guangdianzhangzhuang Quantou
0.01
2.68
0.12
0.01
1.57
0.09
Caiputai
Duancun
Shaochedian
Mean
(mg/L)
0.01
0.8
0.05
0.01
1.19
0.09
0.01
1.74
0.06
0.01
1.71
0.09
The total discharge and the point and non-point source pollution load in the Jiaozhuang section of
Baoding City are shown in Table 12. The input concentration of Pb, TN, and TP from the storm runoff
on 08102008 was two times, 3.8 times, and 2.1 times higher, respectively, than the average concentration
in Baiyangdian Lake.
Water 2017, 9, 249
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Table 12. Total discharge and pollution load of the point source and non-point source of Baoding City.
Pollution Load (kg)
Time
Emission Source
Total Flow of
Discharging (104 m3 )
Pb
Zn
TN
TP
COD
1998
Rainfall-runoff
Industrial Sewage
Total pollution
3453.31
4500.00
7953.31
554.3
1350.0
1904.3
1560.9
2250.0
3810.9
591,187.1
2,475,000.0
3,066,187.1
41,738.1
225,000.0
266,738.1
1,616,665.4
6,750,000.0
8,366,665.4
2008
Rainfall-runoff
Industrial sewage
Total pollution
4813.13
6000.00
10,813.13
852.0
1800.0
2652.0
2362.8
3000.0
5362.8
1,123,163.2
3,300,000
4,423,163.2
82,818.9
300,000.0
382,818.9
3,116,086.2
9,000,000.0
12,116,086.2
As rainfall-runoff and industrial sewage moves from Baoding City from the cross section into
Baiyangdian Lake, and water losses occur along the way, including evaporation loss, river leakage
loss, and loss due to agricultural irrigation. The pollutant load of rainfall-runoff input Baiyangdian
Lake from Baoding City was deducted due to the losses of water along the way. The mean annual
losses account for 40% of total water volume. If only river leakage losses are considered for a single
rainstorm event, the leakage loss accounts for 15% of the total runoff. During the 10 years from 1998 to
2008, the growth in the non-point source pollution loads for Pb, Zn, TN, TP, and COD were 178.6 kg,
481.2 kg, 134,539.6 kg, 7866 kg, and 330,784.6 kg, respectively. The non-point source pollution load
input accounted for the proportion of total pollution load input also increased.
4. Conclusions
In this paper, a simulation model for non-point source pollution of Baoding City was developed
based on SWMM. Two typical measured rainfall-runoff processes on 07312008 and 08212008 were
used to calibrate hydraulic and hydrological parameters of SWMM using trial and error for debugging.
After the calibration of the model simulation error can be controlled within −36.6% to −11.4%,
the actual process and the simulation process have achieved good fitting effect. The fit between
measured and simulated processes was good, demonstrating that the model calibration was successful.
The simulation results showed that a typical rainstorm on 08102008 produced a total runoff of
4.52 × 106 m3 , and the non-point source pollution loads for Pb, Zn, TN, TP, and COD were 145.50,
556.72, 29,412.84, 874.98 and 74,218.42 kg, respectively.
The annual rainfall-runoff volume in 2008 was 48.13 × 106 m3 , and the total runoff of the pipe
network was 31.16 × 106 m3 , which accounted for 60% of the total annual runoff. The annual non-point
source pollution loads for Pb, Zn, TN, TP, and COD were 852.0 kg, 2362.8 kg, 1,123,163 kg, 82,819 kg,
and 3,116,086 kg, respectively, and the pollution load of the pipe network accounted for about 80% of
those values.
The one-dimensional water quality model was applied in the research, the simulation results
showed that the average concentration of TN and TP of the annual rainfall-runoff was about 10 times
higher than that of Baiyangdian Lake. The input water for rainfall-runoff was 20.72 × 106 m3 in 1998,
accounting for 43% of the total amount of rain and sewage in Baoding City. The non-point source
pollution loads for Pb, Zn, TN, TP, and COD were 332.6 kg, 936.5 kg, 341,866.4 kg, 19,868.7 kg, and
356,833.7 kg, respectively. The rainfall-runoff water input was 28.88 × 106 m3 in 2008, accounting
for 45% of the total amount of rain and sewage. The non-point pollution loads for Pb, Zn, TN, TP,
and COD were 511.2 kg, 476,406.0 kg, 1,417.7 kg, 27,734.7 kg, and 687,618.3 kg, respectively. From
1998–2008, the total input of the non-point source pollution load for rainfall-runoff in Baoding City has
increased, and the annual input accounted for about 30% of the total amount of pollutant load.
Based on the simulation results of non-point source pollution, applicable control measures for
non-point source pollution especially for Baoding City would be taken, such as urban rainwater
and flood resources utilization and Best Management Practices (BMPs) for urban non-point source
pollution control, which including engineering and non-engineering measures. In future research, the
Water 2017, 9, 249
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control measures can be enhanced for floods control and pollutant reduction. In this way, the research
would be more practical guidance.
Our monitoring data is limited by the lack of long-term monitoring, the use of only three sets of
data, two used to calibrate and the other one to validate the model, these can meet the requests of
model calibration and validation at a basic level, and the result of this study has its limitations. Further
research will be needed to improve this study.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4441/9/4/249.
Acknowledgments: This research was supported by National key research and development program
(2016YFC0401302). We would like to extend special thanks to the editor and the anonymous reviewers for
their valuable comments in greatly improving the quality of this paper.
Author Contributions: Fen Zhao and Xiaokang Zheng conceived and designed the experiments; Chunhui Li
performed the experiments; Xuan Wang and Yanpeng Cai analyzed the data; Nan Zhang contributed
reagents/materials/analysis tools; Chunhui Li wrote the paper.
Conflicts of Interest: The authors declare no conflict of interest.
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